A hybrid adaptive control strategy for a smart prosthetic hand

Area of Science:

• Biomedical Engineering
• Robotics
• Artificial Intelligence

Background:

• Prosthetic hand control requires precise inverse kinematics and dynamic modeling.
• Existing control systems like PID controllers face limitations in achieving optimal performance.
• Soft and hard computing techniques offer potential for advanced prosthetic control.

Purpose of the Study:

• To develop and evaluate a hybrid control system for a two-dimensional prosthetic hand.
• To integrate adaptive neuro-fuzzy inference system (ANFIS) for inverse kinematics.
• To utilize adaptive control for linearized dynamics to minimize tracking error.

Main Methods:

• A hybrid controller combining ANFIS and adaptive control was designed.
• ANFIS was employed for the inverse kinematics of the prosthetic hand.
• Adaptive control was applied to the linearized dynamics model.
• Simulations were conducted to compare performance against a PID controller.

Main Results:

• The hybrid controller demonstrated enhanced performance over the proportional-integral-derivative (PID) controller.
• The proposed system effectively minimized tracking error in two-dimensional prosthetic hand movements.
• Simulations confirmed the superiority of the ANFIS-based hybrid approach.

Conclusions:

• The hybrid ANFIS and adaptive control system offers a promising advancement in prosthetic hand control.
• This methodology provides a robust solution for achieving precise and accurate prosthetic hand movements.
• Future work aims to extend this technique to more complex, multi-fingered, three-dimensional prosthetic hands.